Interpretable multimodal network analysis of lipid‐lowering therapies

This is the authors' abstract. We don't add key points for this paper.

BACKGROUND AND PURPOSE: Multimodal integration of clinical and omics data could clarify the hepatic mechanisms of lipid-lowering therapies, but interpretable methods for heterogeneous data remain limited. The objective was to apply interpretable multimodal networks (IMNs), a topology-aware framework that condenses heterogeneous omics and clinical data into compact, cross-modal modules, to characterize treatment-specific hepatic responses to simvastatin and ezetimibe. EXPERIMENTAL APPROACH: In the Stockholm Study, patients scheduled for cholecystectomy were randomized for 4 weeks to simvastatin, ezetimibe, their combination or placebo. We integrated liver transcriptomics and DNA methylation with plasma/bile biochemistry, including lipoprotein functionality, on the human protein-protein interactome to build treatment-specific IMNs and evaluated selected network-derived predictions in a human hepatocyte-like model (SOAT2-only-HepG2). KEY RESULTS: IMNs reduced dimensionality while preserving network topology, producing distinct molecular signatures for simvastatin, ezetimibe and combination therapy that mapped to different biological processes; the placebo network was less multimodal and more fragmented. IMNs highlighted apolipoprotein-centred modules and predicted drug effects on APOA2, PPARA and PTPRS, linked to plasma C-peptide. In SOAT2-only-HepG2 cells, selected predictions were supported experimentally, including APOA2 induction and ezetimibe-associated PTPRS upregulation. CONCLUSIONS AND IMPLICATIONS: IMNs provide a practical, interactome-based route from multimodal patient data to focused, testable hypotheses about lipid-lowering therapies, yielding compact features suitable for downstream predictive modelling while retaining biological interpretability.